Marine Biodiversity Observation Network Pole to Pole of the Americas (MBON Pole to Pole)

Written by E. Montes () and Eduardo Klein () on Auguts 28, 2020.

This code pulls data from NOAA’s ERDDAP servers and creates time series plots of sea surface temperature (SST) and chlorophyll-a concentration (CHL), and maps showing the latest available data for the selected region.

Step 1

First, let’s load required libraries

library(readr)
library(rerddap)
library(lubridate)
library(dplyr)
library(flexdashboard)
library(reshape2)
library(leaflet)
library(ggplot2)
library(vegan)
library(xts)
library(dygraphs)
library(plotly)
library(mapdata)

library(RColorBrewer)
palette(brewer.pal(8, "Set2"))

Step 2

Query SST data from ERDDAP

## remove all spaces from string
NoSpaces = function(x){
  return(gsub(" ", "", x))
}

## set site coordinates and time for SST extraction
SSTSiteName = "Patagonia"   ## for the resulting file name
SSTcoords.lon = -64.
SSTcoords.lat = -41.7

SSTstartDate = "2002-06-01"

## set climatological date start-end
SSTclimStartDate = "2002-06-01"
SSTclimEndDate = "2012-12-31"

## set dataset source
SSTsource = info("jplMURSST41")

##
## Get sst 
SST <- griddap(SSTsource, 
              time=c(SSTstartDate, "last"),
              longitude = c(SSTcoords.lon,SSTcoords.lon),
              latitude = c(SSTcoords.lat,SSTcoords.lat),
              fields = "analysed_sst",
              fmt = "csv")

SST = SST[,c(1,4)]
names(SST) = c("time", "SST")

## convert time to a Data object
SST$time = as.Date(ymd_hms(SST$time))

Step 3

Calculate SST climatology

SST.clim = SST %>% filter(time>=ymd(SSTclimStartDate), time<=SSTclimEndDate) %>% 
  group_by(yDay = yday(time)) %>% 
  summarise(SST.mean = mean(SST),
            SST.median = median(SST),
            SST.sd = sd(SST),
            SST.q5 = quantile(SST, 0.05),
            SST.q10 = quantile(SST, 0.10),
            SST.q25 = quantile(SST, 0.25),
            SST.q75 = quantile(SST, 0.75),
            SST.q90 = quantile(SST, 0.90),
            SST.q95 = quantile(SST, 0.95),
            SST.min = min(SST),
            SST.max = max(SST))

Step 4

Plot SST time series

SST.xts = as.xts(SST$SST, SST$time)
dygraph(SST.xts, 
        ylab = "Sea Surface Temperature (Deg C)") %>% 
  dySeries("V1", label ="SST (Deg C)", color = "steelblue") %>%
  dyHighlight(highlightCircleSize = 5, 
              highlightSeriesBackgroundAlpha = 0.2,
              hideOnMouseOut = FALSE) %>% 
  dyOptions(fillGraph = FALSE, fillAlpha = 0.4) %>% 
  dyRangeSelector(dateWindow = c(max(SST$time) - years(5), max(SST$time)))


## subset SST for last year
SST.lastyear = SST %>% filter(year(time)==max(year(time)))

## make the plot
pp = ggplot(SST.clim, aes(yDay, SST.mean))
pp = pp + geom_line() + geom_smooth(span=0.25, se=FALSE, colour="steelblue") +  
  geom_ribbon(aes(ymin=SST.q25, ymax=SST.q75), fill="steelblue", alpha=0.5) +
  geom_line(data=SST.lastyear, aes(yday(time), SST), colour="red") + 
  ylab("Sea Surface Temperature (Deg C)") + xlab("Day of the Year") + 
  theme_bw(base_size = 9) 
ggplotly(pp) %>% plotly::config(displayModeBar = F) 
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

Step 5

Save SST time series data

write_csv(SST, path = paste0(NoSpaces(SSTSiteName), "_SST.csv"))
write_csv(SST.clim, path = paste0(NoSpaces(SSTSiteName), "_Climatology.csv"))

Step 6

Create a map of the latest SST data

sstInfo <- info('jplMURSST41')
# get latest 3-day composite sst
GHRSST <- griddap(sstInfo, latitude = c(-60., -20.), longitude = c(-90., -47.), time = c('last','last'), fields = 'analysed_sst')

mycolor <- colors$temperature
w <- map_data("worldHires", ylim = c(-60., -20.), xlim = c(-90., -47.))
ggplot(data = GHRSST$data, aes(x = lon, y = lat, fill = analysed_sst)) + 
  geom_polygon(data = w, aes(x = long, y = lat, group = group), fill = "grey80") +
  geom_raster(interpolate = FALSE) +
  scale_fill_gradientn(colours = mycolor, na.value = NA) +
  theme_bw() + ylab("latitude") + xlab("longitude") +
  coord_fixed(1.3, xlim = c(-90., -47.),  ylim = c(-60., -20.)) + ggtitle("Latest daily SST data")

Step 7

Query CHL data from ERDDAP

## remove all spaces from string
NoSpaces = function(x){
  return(gsub(" ", "", x))
}

## set site coordinates and time for CHL extraction
CHLSiteName = "Golfo Nuevo"   ## for the resulting file name
CHLcoords.lon = -74
CHLcoords.lat = 12

CHLstartDate = "2012-01-01"

## set climatological date start-end
CHLclimStartDate = "2012-01-01"
CHLclimEndDate = "2016-12-31"

## set dataset source
CHLsource = info("erdMH1chla8day")

##
## Get CHL 
CHL <- griddap(CHLsource, 
               time=c(CHLstartDate, "last"),
               longitude = c(CHLcoords.lon,CHLcoords.lon),
               latitude = c(CHLcoords.lat,CHLcoords.lat),
               fields = "chlorophyll", fmt = "csv")

CHL = CHL[,c(1,4)]
names(CHL) = c("time", "CHL")
CHL = na.omit(CHL)

## convert time to a Data object
CHL$time = as.Date(ymd_hms(CHL$time))

Step 8

Calculate CHL climatology

CHL.clim = CHL %>% filter(time>=ymd(CHLclimStartDate), time<=CHLclimEndDate) %>% 
  group_by(yDay = yday(time)) %>% 
  summarise(CHL.mean = mean(CHL),
            CHL.median = median(CHL),
            CHL.sd = sd(CHL),
            CHL.q5 = quantile(CHL, 0.05),
            CHL.q10 = quantile(CHL, 0.10),
            CHL.q25 = quantile(CHL, 0.25),
            CHL.q75 = quantile(CHL, 0.75),
            CHL.q90 = quantile(CHL, 0.90),
            CHL.q95 = quantile(CHL, 0.95),
            CHL.min = min(CHL),
            CHL.max = max(CHL))

Step 9

Plot CHL time series

CHL.xts = as.xts(CHL$CHL, CHL$time)
dygraph(CHL.xts, 
        ylab = "Chlorophyll a (mg m-3)") %>% 
  dySeries("V1", label ="CHL", color = "steelblue") %>%
  dyHighlight(highlightCircleSize = 5, 
              highlightSeriesBackgroundAlpha = 0.2,
              hideOnMouseOut = FALSE) %>% 
  dyOptions(fillGraph = FALSE, fillAlpha = 0.4) %>% 
  dyRangeSelector(dateWindow = c(max(CHL$time) - years(5), max(CHL$time)))


### CHL Last year with smoothed Climatology {data-width=250}

## subset CHL for last year
CHL.lastyear = CHL %>% filter(year(time)==max(year(time)))

## make the plot
pp = ggplot(CHL.clim, aes(yDay, CHL.mean))
pp = pp + geom_line() + geom_smooth(span=0.25, se=FALSE, colour="steelblue") +  
  geom_ribbon(aes(ymin=CHL.q25, ymax=CHL.q75), fill="steelblue", alpha=0.5) +
  geom_line(data=CHL.lastyear, aes(yday(time), CHL), colour="red") + 
  ylab("Chlorophyll a (mg m-3)") + xlab("Day of the Year") + 
  theme_bw(base_size = 9) 
ggplotly(pp) %>% plotly::config(displayModeBar = F) 
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

#Step 10 Save CHL time series data

write_csv(CHL, path = paste0(NoSpaces(CHLSiteName), "_CHL.csv"))
write_csv(CHL.clim, path = paste0(NoSpaces(CHLSiteName), "_Climatology.csv"))

#Step 11 Create a map of the latest CHL data

require("rerddap")
require("ggplot2")
require("mapdata")

# get latest Monthly chl (VIIRS)
chlaInfo <- info('nesdisVHNSQchlaMonthly')
viirsCHLA <- griddap(chlaInfo, latitude = c(-20., -60.), longitude = c(-90., -47.), time = c('last','last'), fields = 'chlor_a')

# get latest 8-day chl (MODIS)
chlaInfo_8d <- info('erdMH1chla8day')
MODIS_CHLA_8d <- griddap(chlaInfo_8d, latitude = c(-20., -60.), longitude = c(-90., -47.), time = c('last','last'), fields = 'chlorophyll')

# Map monthly chl (VIIRS)
mycolor <- colors$chlorophyll
w <- map_data("worldHires", ylim = c(-60., -20.), xlim = c(-90., -47.))
ggplot(data = viirsCHLA$data, aes(x = lon, y = lat, fill = log(chlor_a))) + 
  geom_polygon(data = w, aes(x = long, y = lat, group = group), fill = "grey80") +
  geom_raster(interpolate = FALSE) +
  scale_fill_gradientn(colours = mycolor, na.value = NA) +
  theme_bw() + ylab("latitude") + xlab("longitude") +
  coord_fixed(1.3, xlim = c(-90., -47.),  ylim = c(-60., -20.)) + ggtitle("Latest VIIRS Monthly Chla")


# Map 8-day chl (MODIS)
mycolor <- colors$chlorophyll
w <- map_data("worldHires", ylim = c(-60., -20.), xlim = c(-90., -47.))
ggplot(data = MODIS_CHLA_8d$data, aes(x = lon, y = lat, fill = log(chlorophyll))) + 
  geom_polygon(data = w, aes(x = long, y = lat, group = group), fill = "grey80") +
  geom_raster(interpolate = FALSE) +
  scale_fill_gradientn(colours = mycolor, na.value = NA) +
  theme_bw() + ylab("latitude") + xlab("longitude") +
  coord_fixed(1.3, xlim = c(-90., -47.),  ylim = c(-60., -20.)) + ggtitle("Latest MODIS 8-day Chla")

---
title: "Satellite SST and CHL data extractions from selected locations"
output: html_notebook
---

## Marine Biodiversity Observation Network Pole to Pole of the Americas (MBON Pole to Pole)

Written by E. Montes (emontesh@usf.edu) and Eduardo Klein (eklein@usb.ve) on Auguts 28, 2020.

This code pulls data from NOAA's [ERDDAP](https://coastwatch.pfeg.noaa.gov/erddap/index.html) servers and creates time series plots of sea surface temperature (SST) and chlorophyll-a concentration (CHL), and maps showing the latest available data for the selected region.

# Step 1
First, let's load required libraries
```{r}
library(readr)
library(rerddap)
library(lubridate)
library(dplyr)
library(flexdashboard)
library(reshape2)
library(leaflet)
library(ggplot2)
library(vegan)
library(xts)
library(dygraphs)
library(plotly)
library(mapdata)

library(RColorBrewer)
palette(brewer.pal(8, "Set2"))
```

# Step 2
Query SST data from ERDDAP
```{r}
## remove all spaces from string
NoSpaces = function(x){
  return(gsub(" ", "", x))
}

## set site coordinates and time for SST extraction
SSTSiteName = "Patagonia"   ## for the resulting file name
SSTcoords.lon = -64.
SSTcoords.lat = -41.7

SSTstartDate = "2002-06-01"

## set climatological date start-end
SSTclimStartDate = "2002-06-01"
SSTclimEndDate = "2012-12-31"

## set dataset source
SSTsource = info("jplMURSST41")

##
## Get sst 
SST <- griddap(SSTsource, 
              time=c(SSTstartDate, "last"),
              longitude = c(SSTcoords.lon,SSTcoords.lon),
              latitude = c(SSTcoords.lat,SSTcoords.lat),
              fields = "analysed_sst",
              fmt = "csv")

SST = SST[,c(1,4)]
names(SST) = c("time", "SST")

## convert time to a Data object
SST$time = as.Date(ymd_hms(SST$time))

```

# Step 3
Calculate SST climatology
```{r}
SST.clim = SST %>% filter(time>=ymd(SSTclimStartDate), time<=SSTclimEndDate) %>% 
  group_by(yDay = yday(time)) %>% 
  summarise(SST.mean = mean(SST),
            SST.median = median(SST),
            SST.sd = sd(SST),
            SST.q5 = quantile(SST, 0.05),
            SST.q10 = quantile(SST, 0.10),
            SST.q25 = quantile(SST, 0.25),
            SST.q75 = quantile(SST, 0.75),
            SST.q90 = quantile(SST, 0.90),
            SST.q95 = quantile(SST, 0.95),
            SST.min = min(SST),
            SST.max = max(SST))
```

# Step 4
Plot SST time series
```{r}
SST.xts = as.xts(SST$SST, SST$time)
dygraph(SST.xts, 
        ylab = "Sea Surface Temperature (Deg C)") %>% 
  dySeries("V1", label ="SST (Deg C)", color = "steelblue") %>%
  dyHighlight(highlightCircleSize = 5, 
              highlightSeriesBackgroundAlpha = 0.2,
              hideOnMouseOut = FALSE) %>% 
  dyOptions(fillGraph = FALSE, fillAlpha = 0.4) %>% 
  dyRangeSelector(dateWindow = c(max(SST$time) - years(5), max(SST$time)))

## subset SST for last year
SST.lastyear = SST %>% filter(year(time)==max(year(time)))

## make the plot
pp = ggplot(SST.clim, aes(yDay, SST.mean))
pp = pp + geom_line() + geom_smooth(span=0.25, se=FALSE, colour="steelblue") +  
  geom_ribbon(aes(ymin=SST.q25, ymax=SST.q75), fill="steelblue", alpha=0.5) +
  geom_line(data=SST.lastyear, aes(yday(time), SST), colour="red") + 
  ylab("Sea Surface Temperature (Deg C)") + xlab("Day of the Year") + 
  theme_bw(base_size = 9) 
ggplotly(pp) %>% plotly::config(displayModeBar = F) 
```

# Step 5
Save SST time series data
```{r}
write_csv(SST, path = paste0(NoSpaces(SSTSiteName), "_SST.csv"))
write_csv(SST.clim, path = paste0(NoSpaces(SSTSiteName), "_Climatology.csv"))
```

# Step 6
Create a map of the latest SST data
```{r}
sstInfo <- info('jplMURSST41')
# get latest 3-day composite sst
GHRSST <- griddap(sstInfo, latitude = c(-60., -20.), longitude = c(-90., -47.), time = c('last','last'), fields = 'analysed_sst')

mycolor <- colors$temperature
w <- map_data("worldHires", ylim = c(-60., -20.), xlim = c(-90., -47.))
ggplot(data = GHRSST$data, aes(x = lon, y = lat, fill = analysed_sst)) + 
  geom_polygon(data = w, aes(x = long, y = lat, group = group), fill = "grey80") +
  geom_raster(interpolate = FALSE) +
  scale_fill_gradientn(colours = mycolor, na.value = NA) +
  theme_bw() + ylab("latitude") + xlab("longitude") +
  coord_fixed(1.3, xlim = c(-90., -47.),  ylim = c(-60., -20.)) + ggtitle("Latest daily SST data")
```


# Step 7
Query CHL data from ERDDAP
```{r}
## remove all spaces from string
NoSpaces = function(x){
  return(gsub(" ", "", x))
}

## set site coordinates and time for CHL extraction
CHLSiteName = "Golfo Nuevo"   ## for the resulting file name
CHLcoords.lon = -74
CHLcoords.lat = 12

CHLstartDate = "2012-01-01"

## set climatological date start-end
CHLclimStartDate = "2012-01-01"
CHLclimEndDate = "2016-12-31"

## set dataset source
CHLsource = info("erdMH1chla8day")

##
## Get CHL 
CHL <- griddap(CHLsource, 
               time=c(CHLstartDate, "last"),
               longitude = c(CHLcoords.lon,CHLcoords.lon),
               latitude = c(CHLcoords.lat,CHLcoords.lat),
               fields = "chlorophyll", fmt = "csv")

CHL = CHL[,c(1,4)]
names(CHL) = c("time", "CHL")
CHL = na.omit(CHL)

## convert time to a Data object
CHL$time = as.Date(ymd_hms(CHL$time))

```

# Step 8
Calculate CHL climatology
```{r}
CHL.clim = CHL %>% filter(time>=ymd(CHLclimStartDate), time<=CHLclimEndDate) %>% 
  group_by(yDay = yday(time)) %>% 
  summarise(CHL.mean = mean(CHL),
            CHL.median = median(CHL),
            CHL.sd = sd(CHL),
            CHL.q5 = quantile(CHL, 0.05),
            CHL.q10 = quantile(CHL, 0.10),
            CHL.q25 = quantile(CHL, 0.25),
            CHL.q75 = quantile(CHL, 0.75),
            CHL.q90 = quantile(CHL, 0.90),
            CHL.q95 = quantile(CHL, 0.95),
            CHL.min = min(CHL),
            CHL.max = max(CHL))
```


# Step 9
Plot CHL time series
```{r}
CHL.xts = as.xts(CHL$CHL, CHL$time)
dygraph(CHL.xts, 
        ylab = "Chlorophyll a (mg m-3)") %>% 
  dySeries("V1", label ="CHL", color = "steelblue") %>%
  dyHighlight(highlightCircleSize = 5, 
              highlightSeriesBackgroundAlpha = 0.2,
              hideOnMouseOut = FALSE) %>% 
  dyOptions(fillGraph = FALSE, fillAlpha = 0.4) %>% 
  dyRangeSelector(dateWindow = c(max(CHL$time) - years(5), max(CHL$time)))

### CHL Last year with smoothed Climatology {data-width=250}

## subset CHL for last year
CHL.lastyear = CHL %>% filter(year(time)==max(year(time)))

## make the plot
pp = ggplot(CHL.clim, aes(yDay, CHL.mean))
pp = pp + geom_line() + geom_smooth(span=0.25, se=FALSE, colour="steelblue") +  
  geom_ribbon(aes(ymin=CHL.q25, ymax=CHL.q75), fill="steelblue", alpha=0.5) +
  geom_line(data=CHL.lastyear, aes(yday(time), CHL), colour="red") + 
  ylab("Chlorophyll a (mg m-3)") + xlab("Day of the Year") + 
  theme_bw(base_size = 9) 
ggplotly(pp) %>% plotly::config(displayModeBar = F) 

```

#Step 10
Save CHL time series data
```{r}
write_csv(CHL, path = paste0(NoSpaces(CHLSiteName), "_CHL.csv"))
write_csv(CHL.clim, path = paste0(NoSpaces(CHLSiteName), "_Climatology.csv"))
```

#Step 11
Create a map of the latest CHL data
```{r}
require("rerddap")
require("ggplot2")
require("mapdata")

# get latest Monthly chl (VIIRS)
chlaInfo <- info('nesdisVHNSQchlaMonthly')
viirsCHLA <- griddap(chlaInfo, latitude = c(-20., -60.), longitude = c(-90., -47.), time = c('last','last'), fields = 'chlor_a')

# get latest 8-day chl (MODIS)
chlaInfo_8d <- info('erdMH1chla8day')
MODIS_CHLA_8d <- griddap(chlaInfo_8d, latitude = c(-20., -60.), longitude = c(-90., -47.), time = c('last','last'), fields = 'chlorophyll')

# Map monthly chl (VIIRS)
mycolor <- colors$chlorophyll
w <- map_data("worldHires", ylim = c(-60., -20.), xlim = c(-90., -47.))
ggplot(data = viirsCHLA$data, aes(x = lon, y = lat, fill = log(chlor_a))) + 
  geom_polygon(data = w, aes(x = long, y = lat, group = group), fill = "grey80") +
  geom_raster(interpolate = FALSE) +
  scale_fill_gradientn(colours = mycolor, na.value = NA) +
  theme_bw() + ylab("latitude") + xlab("longitude") +
  coord_fixed(1.3, xlim = c(-90., -47.),  ylim = c(-60., -20.)) + ggtitle("Latest VIIRS Monthly Chla")

# Map 8-day chl (MODIS)
mycolor <- colors$chlorophyll
w <- map_data("worldHires", ylim = c(-60., -20.), xlim = c(-90., -47.))
ggplot(data = MODIS_CHLA_8d$data, aes(x = lon, y = lat, fill = log(chlorophyll))) + 
  geom_polygon(data = w, aes(x = long, y = lat, group = group), fill = "grey80") +
  geom_raster(interpolate = FALSE) +
  scale_fill_gradientn(colours = mycolor, na.value = NA) +
  theme_bw() + ylab("latitude") + xlab("longitude") +
  coord_fixed(1.3, xlim = c(-90., -47.),  ylim = c(-60., -20.)) + ggtitle("Latest MODIS 8-day Chla")
```

